New submission from Serhiy Storchaka:
In some functions in the random module checking input arguments and
precomputation takes a considerable portion of time. Here is a sample
implementation of new random.distrib module which provides alternative faster
interface to generating of random distributed values. It contains generators
which generates values with same distributions as functions with same name in
the random module.
Benchmark results:
random distrib
random() 0.061 0.055 1.12
randrange(0, 100, 5) 1.494 0.620 2.41
randint(1, 100) 1.283 0.551 2.33
uniform(-10.0, 10.0) 0.332 0.121 2.73
triangular(0.0, 10.0, 6.0) 0.661 0.317 2.09
gauss(5.0, 2.0) 0.707 0.280 2.53
normalvariate(5.0, 2.0) 0.867 0.553 1.57
lognormvariate(5.0, 2.0) 1.078 0.640 1.68
expovariate(0.1,) 0.508 0.293 1.73
vonmisesvariate(1.0, 1.0) 1.201 0.671 1.79
gammavariate(0.35, 1.45) 1.117 0.508 2.20
betavariate(2.71828, 3.14159) 2.868 1.776 1.61
paretovariate(5.0,) 0.493 0.238 2.07
weibullvariate(1.0, 3.0) 0.670 0.402 1.67
choice([0, 1, 2, 3, 4, 5, 6... 0.887 0.594 1.49
Distrib functions are 1.5-2.8 times faster than random functions. Weighted
choice() function (see issue18844) can be even dozens times faster (depends on
size of the input).
In additional some random generators (i.e. gauss()) looks simpler when
implemented as generators. distrib.gauss() is twice faster than
distrib.normalvariate() (both generates numbers with same distribution) and I
think some other generators can be implemented more efficient in generator
style.
----------
components: Library (Lib)
files: distrib.py
messages: 196727
nosy: mark.dickinson, rhettinger, serhiy.storchaka
priority: normal
severity: normal
status: open
title: Add the random.distrib module
type: enhancement
versions: Python 3.4
Added file: http://bugs.python.org/file31549/distrib.py
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<http://bugs.python.org/issue18900>
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